{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Distance to Confidence\n",
    "\n",
    "DeepFace performs a hard classification by clearly separating same person and different persons based on the pre-tuned threshold.\n",
    "\n",
    "This notebook builds a logistic regression model to convert calculated distances to probalistic estimate, indicating how likely the classification is correct, thus giving a softer, more informative measure of certainy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# built-in dependencies\n",
    "import itertools\n",
    "import math\n",
    "\n",
    "# 3rd party dependencies\n",
    "import pandas as pd\n",
    "from deepface import DeepFace\n",
    "from deepface.modules.verification import find_distance, find_threshold\n",
    "from tqdm import tqdm\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "detector_backend = \"mtcnn\" # a robust one\n",
    "\n",
    "model_names = [\n",
    "    \"VGG-Face\", \"Facenet\", \"Facenet512\", \"ArcFace\", \"GhostFaceNet\",\n",
    "    \"Dlib\", \"SFace\", \"OpenFace\", \"DeepFace\", \"DeepID\", \"Buffalo_L\"\n",
    "]\n",
    "\n",
    "distance_metrics = [\n",
    "    \"cosine\", \"euclidean\", \"euclidean_l2\", \"angular\",\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Configurations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_rows', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_name = model_names[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running an experiment for Facenet & mtcnn...\n"
     ]
    }
   ],
   "source": [
    "print(f\"Running an experiment for {model_name} & {detector_backend}...\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# build models in advance\n",
    "model = DeepFace.build_model(model_name)\n",
    "detector = DeepFace.build_model(task=\"face_detector\", model_name=detector_backend)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prepare Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "idendities = {\n",
    " \"Angelina\": [\"img1.jpg\", \"img2.jpg\", \"img4.jpg\"\n",
    "    , \"img5.jpg\", \"img6.jpg\", \"img7.jpg\", \"img10.jpg\", \"img11.jpg\"],\n",
    " \"Scarlett\": [\"img8.jpg\", \"img9.jpg\"],\n",
    " \"Jennifer\": [\"img3.jpg\", \"img12.jpg\"],\n",
    " \"Mark\": [\"img13.jpg\", \"img14.jpg\", \"img15.jpg\"],\n",
    " \"Jack\": [\"img16.jpg\", \"img17.jpg\"],\n",
    " \"Elon\": [\"img18.jpg\", \"img19.jpg\"],\n",
    " \"Jeff\": [\"img20.jpg\", \"img21.jpg\"],\n",
    " \"Marissa\": [\"img22.jpg\", \"img23.jpg\"],\n",
    " \"Sundar\": [\"img24.jpg\", \"img25.jpg\"]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "positives = []\n",
    "for key, values in idendities.items():\n",
    " for i in range(0, len(values)-1):\n",
    "  for j in range(i+1, len(values)):\n",
    "   positive = []\n",
    "   positive.append(values[i])\n",
    "   positive.append(values[j])\n",
    "   positives.append(positive)\n",
    " \n",
    "positives = pd.DataFrame(positives, columns = [\"file_x\", \"file_y\"])\n",
    "positives[\"actual\"] = \"Same Person\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "samples_list = list(idendities.values())\n",
    " \n",
    "negatives = []\n",
    "for i in range(0, len(idendities) - 1):\n",
    " for j in range(i+1, len(idendities)):\n",
    "  cross_product = itertools.product(samples_list[i], samples_list[j])\n",
    "  cross_product = list(cross_product)\n",
    " \n",
    "  for cross_sample in cross_product:\n",
    "   negative = []\n",
    "   negative.append(cross_sample[0])\n",
    "   negative.append(cross_sample[1])\n",
    "   negatives.append(negative)\n",
    " \n",
    "negatives = pd.DataFrame(negatives, columns = [\"file_x\", \"file_y\"])\n",
    "negatives[\"actual\"] = \"Different Persons\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat([positives, negatives]).reset_index(drop = True)\n",
    " \n",
    "df.file_x = \"../tests/dataset/\"+df.file_x\n",
    "df.file_y = \"../tests/dataset/\"+df.file_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>file_x</th>\n",
       "      <th>file_y</th>\n",
       "      <th>actual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>dataset/img1.jpg</td>\n",
       "      <td>dataset/img2.jpg</td>\n",
       "      <td>Same Person</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>dataset/img1.jpg</td>\n",
       "      <td>dataset/img4.jpg</td>\n",
       "      <td>Same Person</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>dataset/img1.jpg</td>\n",
       "      <td>dataset/img5.jpg</td>\n",
       "      <td>Same Person</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>dataset/img1.jpg</td>\n",
       "      <td>dataset/img6.jpg</td>\n",
       "      <td>Same Person</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>dataset/img1.jpg</td>\n",
       "      <td>dataset/img7.jpg</td>\n",
       "      <td>Same Person</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             file_x            file_y       actual\n",
       "0  dataset/img1.jpg  dataset/img2.jpg  Same Person\n",
       "1  dataset/img1.jpg  dataset/img4.jpg  Same Person\n",
       "2  dataset/img1.jpg  dataset/img5.jpg  Same Person\n",
       "3  dataset/img1.jpg  dataset/img6.jpg  Same Person\n",
       "4  dataset/img1.jpg  dataset/img7.jpg  Same Person"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(300, 3)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generate Embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "pivot = {}\n",
    "def represent(img_name: str):\n",
    "    if pivot.get(img_name) is None:\n",
    "        embedding_objs = DeepFace.represent(img_path=img_name, model_name=model_name, detector_backend=detector_backend)\n",
    "\n",
    "        if len(embedding_objs) > 1:\n",
    "            raise ValueError(f\"{img_name} has more than one face!\")\n",
    "            \n",
    "        pivot[img_name] = [embedding_obj[\"embedding\"] for embedding_obj in embedding_objs]\n",
    "    return pivot[img_name]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 300/300 [00:57<00:00,  5.24it/s]\n"
     ]
    }
   ],
   "source": [
    "img1_embeddings = []\n",
    "img2_embeddings = []\n",
    "for index, instance in tqdm(df.iterrows(), total=df.shape[0]):\n",
    "    img1_embeddings = img1_embeddings + represent(instance[\"file_x\"])\n",
    "    img2_embeddings = img2_embeddings + represent(instance[\"file_y\"])\n",
    "\n",
    "df[\"img1_embeddings\"] = img1_embeddings\n",
    "df[\"img2_embeddings\"] = img2_embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>file_x</th>\n",
       "      <th>file_y</th>\n",
       "      <th>actual</th>\n",
       "      <th>img1_embeddings</th>\n",
       "      <th>img2_embeddings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>dataset/img1.jpg</td>\n",
       "      <td>dataset/img2.jpg</td>\n",
       "      <td>Same Person</td>\n",
       "      <td>[0.64044189453125, 0.6990252733230591, 2.09229...</td>\n",
       "      <td>[0.2710142731666565, 0.6065636873245239, 0.926...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>dataset/img1.jpg</td>\n",
       "      <td>dataset/img4.jpg</td>\n",
       "      <td>Same Person</td>\n",
       "      <td>[0.64044189453125, 0.6990252733230591, 2.09229...</td>\n",
       "      <td>[-0.08416734635829926, 0.17612755298614502, 1....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>dataset/img1.jpg</td>\n",
       "      <td>dataset/img5.jpg</td>\n",
       "      <td>Same Person</td>\n",
       "      <td>[0.64044189453125, 0.6990252733230591, 2.09229...</td>\n",
       "      <td>[-1.072468876838684, 0.48678186535835266, 1.18...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>dataset/img1.jpg</td>\n",
       "      <td>dataset/img6.jpg</td>\n",
       "      <td>Same Person</td>\n",
       "      <td>[0.64044189453125, 0.6990252733230591, 2.09229...</td>\n",
       "      <td>[-0.1698027402162552, 1.0826836824417114, 1.52...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>dataset/img1.jpg</td>\n",
       "      <td>dataset/img7.jpg</td>\n",
       "      <td>Same Person</td>\n",
       "      <td>[0.64044189453125, 0.6990252733230591, 2.09229...</td>\n",
       "      <td>[-0.4807380735874176, 1.4791054725646973, 1.52...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             file_x            file_y       actual  \\\n",
       "0  dataset/img1.jpg  dataset/img2.jpg  Same Person   \n",
       "1  dataset/img1.jpg  dataset/img4.jpg  Same Person   \n",
       "2  dataset/img1.jpg  dataset/img5.jpg  Same Person   \n",
       "3  dataset/img1.jpg  dataset/img6.jpg  Same Person   \n",
       "4  dataset/img1.jpg  dataset/img7.jpg  Same Person   \n",
       "\n",
       "                                     img1_embeddings  \\\n",
       "0  [0.64044189453125, 0.6990252733230591, 2.09229...   \n",
       "1  [0.64044189453125, 0.6990252733230591, 2.09229...   \n",
       "2  [0.64044189453125, 0.6990252733230591, 2.09229...   \n",
       "3  [0.64044189453125, 0.6990252733230591, 2.09229...   \n",
       "4  [0.64044189453125, 0.6990252733230591, 2.09229...   \n",
       "\n",
       "                                     img2_embeddings  \n",
       "0  [0.2710142731666565, 0.6065636873245239, 0.926...  \n",
       "1  [-0.08416734635829926, 0.17612755298614502, 1....  \n",
       "2  [-1.072468876838684, 0.48678186535835266, 1.18...  \n",
       "3  [-0.1698027402162552, 1.0826836824417114, 1.52...  \n",
       "4  [-0.4807380735874176, 1.4791054725646973, 1.52...  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Distance Calculation From Embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 300/300 [00:00<00:00, 4605.25it/s]\n",
      "100%|██████████| 300/300 [00:00<00:00, 13119.64it/s]\n",
      "100%|██████████| 300/300 [00:00<00:00, 9297.74it/s]\n",
      "100%|██████████| 300/300 [00:00<00:00, 12523.55it/s]\n"
     ]
    }
   ],
   "source": [
    "for distance_metric in distance_metrics:\n",
    "    distances = []\n",
    "    for index, instance in tqdm(df.iterrows(), total=df.shape[0]):\n",
    "        img1_embeddings = instance[\"img1_embeddings\"]\n",
    "        img2_embeddings = instance[\"img2_embeddings\"]\n",
    "\n",
    "        distance = find_distance(\n",
    "            alpha_embedding=img1_embeddings,\n",
    "            beta_embedding=img2_embeddings,\n",
    "            distance_metric=distance_metric\n",
    "        )\n",
    "        distances.append(distance)\n",
    "    \n",
    "    df[distance_metric] = distances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>file_x</th>\n",
       "      <th>file_y</th>\n",
       "      <th>actual</th>\n",
       "      <th>img1_embeddings</th>\n",
       "      <th>img2_embeddings</th>\n",
       "      <th>cosine</th>\n",
       "      <th>euclidean</th>\n",
       "      <th>euclidean_l2</th>\n",
       "      <th>angular</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>dataset/img1.jpg</td>\n",
       "      <td>dataset/img2.jpg</td>\n",
       "      <td>Same Person</td>\n",
       "      <td>[0.64044189453125, 0.6990252733230591, 2.09229...</td>\n",
       "      <td>[0.2710142731666565, 0.6065636873245239, 0.926...</td>\n",
       "      <td>0.220304</td>\n",
       "      <td>7.776061</td>\n",
       "      <td>0.663783</td>\n",
       "      <td>0.215374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>dataset/img1.jpg</td>\n",
       "      <td>dataset/img4.jpg</td>\n",
       "      <td>Same Person</td>\n",
       "      <td>[0.64044189453125, 0.6990252733230591, 2.09229...</td>\n",
       "      <td>[-0.08416734635829926, 0.17612755298614502, 1....</td>\n",
       "      <td>0.249256</td>\n",
       "      <td>8.289897</td>\n",
       "      <td>0.706053</td>\n",
       "      <td>0.229695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>dataset/img1.jpg</td>\n",
       "      <td>dataset/img5.jpg</td>\n",
       "      <td>Same Person</td>\n",
       "      <td>[0.64044189453125, 0.6990252733230591, 2.09229...</td>\n",
       "      <td>[-1.072468876838684, 0.48678186535835266, 1.18...</td>\n",
       "      <td>0.291291</td>\n",
       "      <td>8.865397</td>\n",
       "      <td>0.763271</td>\n",
       "      <td>0.249278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>dataset/img1.jpg</td>\n",
       "      <td>dataset/img6.jpg</td>\n",
       "      <td>Same Person</td>\n",
       "      <td>[0.64044189453125, 0.6990252733230591, 2.09229...</td>\n",
       "      <td>[-0.1698027402162552, 1.0826836824417114, 1.52...</td>\n",
       "      <td>0.219916</td>\n",
       "      <td>7.852590</td>\n",
       "      <td>0.663198</td>\n",
       "      <td>0.215176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>dataset/img1.jpg</td>\n",
       "      <td>dataset/img7.jpg</td>\n",
       "      <td>Same Person</td>\n",
       "      <td>[0.64044189453125, 0.6990252733230591, 2.09229...</td>\n",
       "      <td>[-0.4807380735874176, 1.4791054725646973, 1.52...</td>\n",
       "      <td>0.184313</td>\n",
       "      <td>7.179951</td>\n",
       "      <td>0.607146</td>\n",
       "      <td>0.196359</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             file_x            file_y       actual  \\\n",
       "0  dataset/img1.jpg  dataset/img2.jpg  Same Person   \n",
       "1  dataset/img1.jpg  dataset/img4.jpg  Same Person   \n",
       "2  dataset/img1.jpg  dataset/img5.jpg  Same Person   \n",
       "3  dataset/img1.jpg  dataset/img6.jpg  Same Person   \n",
       "4  dataset/img1.jpg  dataset/img7.jpg  Same Person   \n",
       "\n",
       "                                     img1_embeddings  \\\n",
       "0  [0.64044189453125, 0.6990252733230591, 2.09229...   \n",
       "1  [0.64044189453125, 0.6990252733230591, 2.09229...   \n",
       "2  [0.64044189453125, 0.6990252733230591, 2.09229...   \n",
       "3  [0.64044189453125, 0.6990252733230591, 2.09229...   \n",
       "4  [0.64044189453125, 0.6990252733230591, 2.09229...   \n",
       "\n",
       "                                     img2_embeddings    cosine  euclidean  \\\n",
       "0  [0.2710142731666565, 0.6065636873245239, 0.926...  0.220304   7.776061   \n",
       "1  [-0.08416734635829926, 0.17612755298614502, 1....  0.249256   8.289897   \n",
       "2  [-1.072468876838684, 0.48678186535835266, 1.18...  0.291291   8.865397   \n",
       "3  [-0.1698027402162552, 1.0826836824417114, 1.52...  0.219916   7.852590   \n",
       "4  [-0.4807380735874176, 1.4791054725646973, 1.52...  0.184313   7.179951   \n",
       "\n",
       "   euclidean_l2   angular  \n",
       "0      0.663783  0.215374  \n",
       "1      0.706053  0.229695  \n",
       "2      0.763271  0.249278  \n",
       "3      0.663198  0.215176  \n",
       "4      0.607146  0.196359  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_backup = df.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Distance To Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df_backup.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "for distance_metric in distance_metrics:\n",
    "    threshold = find_threshold(model_name=model_name, distance_metric=distance_metric)\n",
    "    df[f\"{distance_metric}_threshold\"] = threshold\n",
    "    df[f\"{distance_metric}_decision\"] = 0\n",
    "    idx = df[df[distance_metric] <= threshold].index\n",
    "    df.loc[idx, f\"{distance_metric}_decision\"] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>file_x</th>\n",
       "      <th>file_y</th>\n",
       "      <th>actual</th>\n",
       "      <th>img1_embeddings</th>\n",
       "      <th>img2_embeddings</th>\n",
       "      <th>cosine</th>\n",
       "      <th>euclidean</th>\n",
       "      <th>euclidean_l2</th>\n",
       "      <th>angular</th>\n",
       "      <th>cosine_threshold</th>\n",
       "      <th>cosine_decision</th>\n",
       "      <th>euclidean_threshold</th>\n",
       "      <th>euclidean_decision</th>\n",
       "      <th>euclidean_l2_threshold</th>\n",
       "      <th>euclidean_l2_decision</th>\n",
       "      <th>angular_threshold</th>\n",
       "      <th>angular_decision</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>dataset/img1.jpg</td>\n",
       "      <td>dataset/img2.jpg</td>\n",
       "      <td>Same Person</td>\n",
       "      <td>[0.64044189453125, 0.6990252733230591, 2.09229...</td>\n",
       "      <td>[0.2710142731666565, 0.6065636873245239, 0.926...</td>\n",
       "      <td>0.220304</td>\n",
       "      <td>7.776061</td>\n",
       "      <td>0.663783</td>\n",
       "      <td>0.215374</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1</td>\n",
       "      <td>0.33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>dataset/img1.jpg</td>\n",
       "      <td>dataset/img4.jpg</td>\n",
       "      <td>Same Person</td>\n",
       "      <td>[0.64044189453125, 0.6990252733230591, 2.09229...</td>\n",
       "      <td>[-0.08416734635829926, 0.17612755298614502, 1....</td>\n",
       "      <td>0.249256</td>\n",
       "      <td>8.289897</td>\n",
       "      <td>0.706053</td>\n",
       "      <td>0.229695</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1</td>\n",
       "      <td>0.33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>dataset/img1.jpg</td>\n",
       "      <td>dataset/img5.jpg</td>\n",
       "      <td>Same Person</td>\n",
       "      <td>[0.64044189453125, 0.6990252733230591, 2.09229...</td>\n",
       "      <td>[-1.072468876838684, 0.48678186535835266, 1.18...</td>\n",
       "      <td>0.291291</td>\n",
       "      <td>8.865397</td>\n",
       "      <td>0.763271</td>\n",
       "      <td>0.249278</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1</td>\n",
       "      <td>0.33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>dataset/img1.jpg</td>\n",
       "      <td>dataset/img6.jpg</td>\n",
       "      <td>Same Person</td>\n",
       "      <td>[0.64044189453125, 0.6990252733230591, 2.09229...</td>\n",
       "      <td>[-0.1698027402162552, 1.0826836824417114, 1.52...</td>\n",
       "      <td>0.219916</td>\n",
       "      <td>7.852590</td>\n",
       "      <td>0.663198</td>\n",
       "      <td>0.215176</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1</td>\n",
       "      <td>0.33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>dataset/img1.jpg</td>\n",
       "      <td>dataset/img7.jpg</td>\n",
       "      <td>Same Person</td>\n",
       "      <td>[0.64044189453125, 0.6990252733230591, 2.09229...</td>\n",
       "      <td>[-0.4807380735874176, 1.4791054725646973, 1.52...</td>\n",
       "      <td>0.184313</td>\n",
       "      <td>7.179951</td>\n",
       "      <td>0.607146</td>\n",
       "      <td>0.196359</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1</td>\n",
       "      <td>0.33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             file_x            file_y       actual  \\\n",
       "0  dataset/img1.jpg  dataset/img2.jpg  Same Person   \n",
       "1  dataset/img1.jpg  dataset/img4.jpg  Same Person   \n",
       "2  dataset/img1.jpg  dataset/img5.jpg  Same Person   \n",
       "3  dataset/img1.jpg  dataset/img6.jpg  Same Person   \n",
       "4  dataset/img1.jpg  dataset/img7.jpg  Same Person   \n",
       "\n",
       "                                     img1_embeddings  \\\n",
       "0  [0.64044189453125, 0.6990252733230591, 2.09229...   \n",
       "1  [0.64044189453125, 0.6990252733230591, 2.09229...   \n",
       "2  [0.64044189453125, 0.6990252733230591, 2.09229...   \n",
       "3  [0.64044189453125, 0.6990252733230591, 2.09229...   \n",
       "4  [0.64044189453125, 0.6990252733230591, 2.09229...   \n",
       "\n",
       "                                     img2_embeddings    cosine  euclidean  \\\n",
       "0  [0.2710142731666565, 0.6065636873245239, 0.926...  0.220304   7.776061   \n",
       "1  [-0.08416734635829926, 0.17612755298614502, 1....  0.249256   8.289897   \n",
       "2  [-1.072468876838684, 0.48678186535835266, 1.18...  0.291291   8.865397   \n",
       "3  [-0.1698027402162552, 1.0826836824417114, 1.52...  0.219916   7.852590   \n",
       "4  [-0.4807380735874176, 1.4791054725646973, 1.52...  0.184313   7.179951   \n",
       "\n",
       "   euclidean_l2   angular  cosine_threshold  cosine_decision  \\\n",
       "0      0.663783  0.215374               0.4                1   \n",
       "1      0.706053  0.229695               0.4                1   \n",
       "2      0.763271  0.249278               0.4                1   \n",
       "3      0.663198  0.215176               0.4                1   \n",
       "4      0.607146  0.196359               0.4                1   \n",
       "\n",
       "   euclidean_threshold  euclidean_decision  euclidean_l2_threshold  \\\n",
       "0                   10                   1                     0.8   \n",
       "1                   10                   1                     0.8   \n",
       "2                   10                   1                     0.8   \n",
       "3                   10                   1                     0.8   \n",
       "4                   10                   1                     0.8   \n",
       "\n",
       "   euclidean_l2_decision  angular_threshold  angular_decision  \n",
       "0                      1               0.33                 1  \n",
       "1                      1               0.33                 1  \n",
       "2                      1               0.33                 1  \n",
       "3                      1               0.33                 1  \n",
       "4                      1               0.33                 1  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Confidence Score Calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "confidence_metrics = {}\n",
    "\n",
    "for distance_metric in distance_metrics:\n",
    "    max_value = df[distance_metric].max()\n",
    "\n",
    "    X = df[distance_metric].values.reshape(-1, 1)\n",
    "\n",
    "    # normalize the distance values before feeding them to the model\n",
    "    if max_value > 1:\n",
    "        X = X / max_value\n",
    "\n",
    "    y = df[f\"{distance_metric}_decision\"].values\n",
    "\n",
    "    model = LogisticRegression().fit(X, y)\n",
    "\n",
    "    w = model.coef_[0][0]\n",
    "    b = model.intercept_[0]\n",
    "\n",
    "    confidence_metrics[distance_metric] = {\n",
    "        \"w\": w,\n",
    "        \"b\": b,\n",
    "        \"normalizer\": max_value,\n",
    "    }\n",
    "\n",
    "    confidences =[]\n",
    "    for index, instance in df.iterrows():\n",
    "        distance = instance[distance_metric]\n",
    "\n",
    "        if max_value > 1:\n",
    "            distance = distance / max_value\n",
    "\n",
    "        z = w * distance + b\n",
    "        confidence = 100 / (1 + math.exp(-z))\n",
    "\n",
    "        confidences.append(confidence)\n",
    "    \n",
    "    df[distance_metric + \"_confidence\"] = confidences\n",
    "\n",
    "    confidence_metrics[distance_metric][\"denorm_max_true\"] = df[df[f\"{distance_metric}_decision\"] == 1][distance_metric + \"_confidence\"].max()\n",
    "    confidence_metrics[distance_metric][\"denorm_min_true\"] = df[df[f\"{distance_metric}_decision\"] == 1][distance_metric + \"_confidence\"].min()\n",
    "\n",
    "    confidence_metrics[distance_metric][\"denorm_max_false\"] = df[df[f\"{distance_metric}_decision\"] == 0][distance_metric + \"_confidence\"].max()\n",
    "    confidence_metrics[distance_metric][\"denorm_min_false\"] = df[df[f\"{distance_metric}_decision\"] == 0][distance_metric + \"_confidence\"].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'cosine': {'w': -6.502269165856082,\n",
       "  'b': 1.679048923097668,\n",
       "  'normalizer': 1.206694,\n",
       "  'denorm_max_true': 77.17253153662926,\n",
       "  'denorm_min_true': 41.790002608273234,\n",
       "  'denorm_max_false': 20.618350202170916,\n",
       "  'denorm_min_false': 0.7976712344840693},\n",
       " 'euclidean': {'w': -6.716177467853723,\n",
       "  'b': 2.790978346203265,\n",
       "  'normalizer': 18.735288,\n",
       "  'denorm_max_true': 74.76412617567517,\n",
       "  'denorm_min_true': 40.4423755909089,\n",
       "  'denorm_max_false': 25.840858374979504,\n",
       "  'denorm_min_false': 1.9356150486888306},\n",
       " 'euclidean_l2': {'w': -6.708710331202137,\n",
       "  'b': 2.9094193067398195,\n",
       "  'normalizer': 1.553508,\n",
       "  'denorm_max_true': 75.45756719896039,\n",
       "  'denorm_min_true': 40.4509428022908,\n",
       "  'denorm_max_false': 30.555931000001184,\n",
       "  'denorm_min_false': 2.189644991619842},\n",
       " 'angular': {'w': -6.371147050396505,\n",
       "  'b': 0.6766460615182355,\n",
       "  'normalizer': 0.56627,\n",
       "  'denorm_max_true': 45.802357900723386,\n",
       "  'denorm_min_true': 24.327312950719133,\n",
       "  'denorm_max_false': 16.95267765757785,\n",
       "  'denorm_min_false': 5.063533287198758}}"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confidence_metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# denormalize confidence scores\n",
    "for distance_metric in distance_metrics:\n",
    "    for index, instance in df.iterrows():\n",
    "        current_distance = instance[distance_metric]\n",
    "        threshold = find_threshold(model_name=model_name, distance_metric=distance_metric)\n",
    "\n",
    "        prediction = \"same person\" if current_distance <= threshold else \"different persons\"\n",
    "\n",
    "        # denormalize same person predictions\n",
    "        if prediction == \"same person\":\n",
    "            min_orginal = confidence_metrics[distance_metric][\"denorm_min_true\"]\n",
    "            max_orginal = confidence_metrics[distance_metric][\"denorm_max_true\"]\n",
    "            min_target = max(51, min_orginal)\n",
    "            max_target = 100\n",
    "        else:\n",
    "            min_orginal = confidence_metrics[distance_metric][\"denorm_min_false\"]\n",
    "            max_orginal = confidence_metrics[distance_metric][\"denorm_max_false\"]\n",
    "            min_target = 0\n",
    "            max_target = min(49, max_orginal)\n",
    "\n",
    "        confidence = instance[f\"{distance_metric}_confidence\"]\n",
    "\n",
    "        confidence_new = (\n",
    "            (confidence - min_orginal) / (max_orginal - min_orginal)\n",
    "        ) * (max_target - min_target) + min_target\n",
    "        \n",
    "        confidence_new = float(confidence_new)\n",
    "\n",
    "        # print(f\"{prediction}: {confidence}  -> {confidence_new}\")\n",
    "\n",
    "        df.loc[index, f\"{distance_metric}_confidence\"] = confidence_new"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>file_x</th>\n",
       "      <th>file_y</th>\n",
       "      <th>actual</th>\n",
       "      <th>cosine</th>\n",
       "      <th>euclidean</th>\n",
       "      <th>euclidean_l2</th>\n",
       "      <th>angular</th>\n",
       "      <th>cosine_confidence</th>\n",
       "      <th>euclidean_confidence</th>\n",
       "      <th>euclidean_l2_confidence</th>\n",
       "      <th>angular_confidence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>dataset/img1.jpg</td>\n",
       "      <td>dataset/img2.jpg</td>\n",
       "      <td>Same Person</td>\n",
       "      <td>0.220304</td>\n",
       "      <td>7.776061</td>\n",
       "      <td>0.663783</td>\n",
       "      <td>0.215374</td>\n",
       "      <td>79.066154</td>\n",
       "      <td>64.767739</td>\n",
       "      <td>65.867875</td>\n",
       "      <td>71.428257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>dataset/img1.jpg</td>\n",
       "      <td>dataset/img4.jpg</td>\n",
       "      <td>Same Person</td>\n",
       "      <td>0.249256</td>\n",
       "      <td>8.289897</td>\n",
       "      <td>0.706053</td>\n",
       "      <td>0.229695</td>\n",
       "      <td>73.892125</td>\n",
       "      <td>58.210897</td>\n",
       "      <td>59.488339</td>\n",
       "      <td>66.878140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>dataset/img1.jpg</td>\n",
       "      <td>dataset/img5.jpg</td>\n",
       "      <td>Same Person</td>\n",
       "      <td>0.291291</td>\n",
       "      <td>8.865397</td>\n",
       "      <td>0.763271</td>\n",
       "      <td>0.249278</td>\n",
       "      <td>66.154520</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>60.905288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>dataset/img1.jpg</td>\n",
       "      <td>dataset/img6.jpg</td>\n",
       "      <td>Same Person</td>\n",
       "      <td>0.219916</td>\n",
       "      <td>7.852590</td>\n",
       "      <td>0.663198</td>\n",
       "      <td>0.215176</td>\n",
       "      <td>79.134312</td>\n",
       "      <td>63.788618</td>\n",
       "      <td>65.956234</td>\n",
       "      <td>71.492183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>dataset/img1.jpg</td>\n",
       "      <td>dataset/img7.jpg</td>\n",
       "      <td>Same Person</td>\n",
       "      <td>0.184313</td>\n",
       "      <td>7.179951</td>\n",
       "      <td>0.607146</td>\n",
       "      <td>0.196359</td>\n",
       "      <td>85.226842</td>\n",
       "      <td>72.364455</td>\n",
       "      <td>74.358148</td>\n",
       "      <td>77.685211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>dataset/img1.jpg</td>\n",
       "      <td>dataset/img10.jpg</td>\n",
       "      <td>Same Person</td>\n",
       "      <td>0.243940</td>\n",
       "      <td>8.278702</td>\n",
       "      <td>0.698483</td>\n",
       "      <td>0.227122</td>\n",
       "      <td>74.854050</td>\n",
       "      <td>58.352994</td>\n",
       "      <td>60.627930</td>\n",
       "      <td>67.684707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>dataset/img1.jpg</td>\n",
       "      <td>dataset/img11.jpg</td>\n",
       "      <td>Same Person</td>\n",
       "      <td>0.236210</td>\n",
       "      <td>8.214206</td>\n",
       "      <td>0.687329</td>\n",
       "      <td>0.223337</td>\n",
       "      <td>76.243849</td>\n",
       "      <td>59.172585</td>\n",
       "      <td>62.310511</td>\n",
       "      <td>68.880036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>dataset/img2.jpg</td>\n",
       "      <td>dataset/img4.jpg</td>\n",
       "      <td>Same Person</td>\n",
       "      <td>0.180357</td>\n",
       "      <td>6.822683</td>\n",
       "      <td>0.600594</td>\n",
       "      <td>0.194172</td>\n",
       "      <td>85.882118</td>\n",
       "      <td>76.844969</td>\n",
       "      <td>75.326061</td>\n",
       "      <td>78.419335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>dataset/img2.jpg</td>\n",
       "      <td>dataset/img5.jpg</td>\n",
       "      <td>Same Person</td>\n",
       "      <td>0.175950</td>\n",
       "      <td>6.651977</td>\n",
       "      <td>0.593211</td>\n",
       "      <td>0.191709</td>\n",
       "      <td>86.606529</td>\n",
       "      <td>78.953612</td>\n",
       "      <td>76.411591</td>\n",
       "      <td>79.249453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>dataset/img2.jpg</td>\n",
       "      <td>dataset/img6.jpg</td>\n",
       "      <td>Same Person</td>\n",
       "      <td>0.085547</td>\n",
       "      <td>4.755967</td>\n",
       "      <td>0.413635</td>\n",
       "      <td>0.132621</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             file_x             file_y       actual    cosine  euclidean  \\\n",
       "0  dataset/img1.jpg   dataset/img2.jpg  Same Person  0.220304   7.776061   \n",
       "1  dataset/img1.jpg   dataset/img4.jpg  Same Person  0.249256   8.289897   \n",
       "2  dataset/img1.jpg   dataset/img5.jpg  Same Person  0.291291   8.865397   \n",
       "3  dataset/img1.jpg   dataset/img6.jpg  Same Person  0.219916   7.852590   \n",
       "4  dataset/img1.jpg   dataset/img7.jpg  Same Person  0.184313   7.179951   \n",
       "5  dataset/img1.jpg  dataset/img10.jpg  Same Person  0.243940   8.278702   \n",
       "6  dataset/img1.jpg  dataset/img11.jpg  Same Person  0.236210   8.214206   \n",
       "7  dataset/img2.jpg   dataset/img4.jpg  Same Person  0.180357   6.822683   \n",
       "8  dataset/img2.jpg   dataset/img5.jpg  Same Person  0.175950   6.651977   \n",
       "9  dataset/img2.jpg   dataset/img6.jpg  Same Person  0.085547   4.755967   \n",
       "\n",
       "   euclidean_l2   angular  cosine_confidence  euclidean_confidence  \\\n",
       "0      0.663783  0.215374          79.066154             64.767739   \n",
       "1      0.706053  0.229695          73.892125             58.210897   \n",
       "2      0.763271  0.249278          66.154520             51.000000   \n",
       "3      0.663198  0.215176          79.134312             63.788618   \n",
       "4      0.607146  0.196359          85.226842             72.364455   \n",
       "5      0.698483  0.227122          74.854050             58.352994   \n",
       "6      0.687329  0.223337          76.243849             59.172585   \n",
       "7      0.600594  0.194172          85.882118             76.844969   \n",
       "8      0.593211  0.191709          86.606529             78.953612   \n",
       "9      0.413635  0.132621         100.000000            100.000000   \n",
       "\n",
       "   euclidean_l2_confidence  angular_confidence  \n",
       "0                65.867875           71.428257  \n",
       "1                59.488339           66.878140  \n",
       "2                51.000000           60.905288  \n",
       "3                65.956234           71.492183  \n",
       "4                74.358148           77.685211  \n",
       "5                60.627930           67.684707  \n",
       "6                62.310511           68.880036  \n",
       "7                75.326061           78.419335  \n",
       "8                76.411591           79.249453  \n",
       "9               100.000000          100.000000  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"actual\"] == \"Same Person\"][[\n",
    "    \"file_x\",\n",
    "    \"file_y\",\n",
    "    \"actual\",\n",
    "    \"cosine\",\n",
    "    \"euclidean\",\n",
    "    \"euclidean_l2\",\n",
    "    \"angular\",\n",
    "    \"cosine_confidence\",\n",
    "    \"euclidean_confidence\",\n",
    "    \"euclidean_l2_confidence\",\n",
    "    \"angular_confidence\",\n",
    "]].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>file_x</th>\n",
       "      <th>file_y</th>\n",
       "      <th>actual</th>\n",
       "      <th>cosine</th>\n",
       "      <th>euclidean</th>\n",
       "      <th>euclidean_l2</th>\n",
       "      <th>angular</th>\n",
       "      <th>cosine_confidence</th>\n",
       "      <th>euclidean_confidence</th>\n",
       "      <th>euclidean_l2_confidence</th>\n",
       "      <th>angular_confidence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>dataset/img1.jpg</td>\n",
       "      <td>dataset/img8.jpg</td>\n",
       "      <td>Different Persons</td>\n",
       "      <td>0.912684</td>\n",
       "      <td>15.971380</td>\n",
       "      <td>1.351062</td>\n",
       "      <td>0.472171</td>\n",
       "      <td>3.094661</td>\n",
       "      <td>3.364286</td>\n",
       "      <td>3.127406</td>\n",
       "      <td>5.404447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>dataset/img1.jpg</td>\n",
       "      <td>dataset/img9.jpg</td>\n",
       "      <td>Different Persons</td>\n",
       "      <td>0.971642</td>\n",
       "      <td>16.535808</td>\n",
       "      <td>1.394017</td>\n",
       "      <td>0.490972</td>\n",
       "      <td>2.056160</td>\n",
       "      <td>2.406347</td>\n",
       "      <td>2.238199</td>\n",
       "      <td>4.092357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>dataset/img2.jpg</td>\n",
       "      <td>dataset/img8.jpg</td>\n",
       "      <td>Different Persons</td>\n",
       "      <td>0.718171</td>\n",
       "      <td>13.744342</td>\n",
       "      <td>1.198475</td>\n",
       "      <td>0.409059</td>\n",
       "      <td>9.632836</td>\n",
       "      <td>9.327166</td>\n",
       "      <td>7.763610</td>\n",
       "      <td>10.860674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>dataset/img2.jpg</td>\n",
       "      <td>dataset/img9.jpg</td>\n",
       "      <td>Different Persons</td>\n",
       "      <td>0.784581</td>\n",
       "      <td>14.418712</td>\n",
       "      <td>1.252662</td>\n",
       "      <td>0.430888</td>\n",
       "      <td>6.713687</td>\n",
       "      <td>7.083039</td>\n",
       "      <td>5.811885</td>\n",
       "      <td>8.776472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>dataset/img4.jpg</td>\n",
       "      <td>dataset/img8.jpg</td>\n",
       "      <td>Different Persons</td>\n",
       "      <td>0.753150</td>\n",
       "      <td>14.123269</td>\n",
       "      <td>1.227314</td>\n",
       "      <td>0.420605</td>\n",
       "      <td>7.987852</td>\n",
       "      <td>8.012303</td>\n",
       "      <td>6.677761</td>\n",
       "      <td>9.730812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>dataset/img4.jpg</td>\n",
       "      <td>dataset/img9.jpg</td>\n",
       "      <td>Different Persons</td>\n",
       "      <td>0.829773</td>\n",
       "      <td>14.878502</td>\n",
       "      <td>1.288234</td>\n",
       "      <td>0.445550</td>\n",
       "      <td>5.177442</td>\n",
       "      <td>5.790447</td>\n",
       "      <td>4.724925</td>\n",
       "      <td>7.497093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>dataset/img5.jpg</td>\n",
       "      <td>dataset/img8.jpg</td>\n",
       "      <td>Different Persons</td>\n",
       "      <td>0.721133</td>\n",
       "      <td>13.643271</td>\n",
       "      <td>1.200943</td>\n",
       "      <td>0.410041</td>\n",
       "      <td>9.483595</td>\n",
       "      <td>9.702528</td>\n",
       "      <td>7.666288</td>\n",
       "      <td>10.762127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>dataset/img5.jpg</td>\n",
       "      <td>dataset/img9.jpg</td>\n",
       "      <td>Different Persons</td>\n",
       "      <td>0.729796</td>\n",
       "      <td>13.777185</td>\n",
       "      <td>1.208136</td>\n",
       "      <td>0.412909</td>\n",
       "      <td>9.057935</td>\n",
       "      <td>9.207479</td>\n",
       "      <td>7.387416</td>\n",
       "      <td>10.476942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>dataset/img6.jpg</td>\n",
       "      <td>dataset/img8.jpg</td>\n",
       "      <td>Different Persons</td>\n",
       "      <td>0.717417</td>\n",
       "      <td>13.921246</td>\n",
       "      <td>1.197846</td>\n",
       "      <td>0.408809</td>\n",
       "      <td>9.671131</td>\n",
       "      <td>8.695498</td>\n",
       "      <td>7.788549</td>\n",
       "      <td>10.885836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>dataset/img6.jpg</td>\n",
       "      <td>dataset/img9.jpg</td>\n",
       "      <td>Different Persons</td>\n",
       "      <td>0.741129</td>\n",
       "      <td>14.200127</td>\n",
       "      <td>1.217481</td>\n",
       "      <td>0.416650</td>\n",
       "      <td>8.524856</td>\n",
       "      <td>7.762737</td>\n",
       "      <td>7.035554</td>\n",
       "      <td>10.110791</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              file_x            file_y             actual    cosine  \\\n",
       "38  dataset/img1.jpg  dataset/img8.jpg  Different Persons  0.912684   \n",
       "39  dataset/img1.jpg  dataset/img9.jpg  Different Persons  0.971642   \n",
       "40  dataset/img2.jpg  dataset/img8.jpg  Different Persons  0.718171   \n",
       "41  dataset/img2.jpg  dataset/img9.jpg  Different Persons  0.784581   \n",
       "42  dataset/img4.jpg  dataset/img8.jpg  Different Persons  0.753150   \n",
       "43  dataset/img4.jpg  dataset/img9.jpg  Different Persons  0.829773   \n",
       "44  dataset/img5.jpg  dataset/img8.jpg  Different Persons  0.721133   \n",
       "45  dataset/img5.jpg  dataset/img9.jpg  Different Persons  0.729796   \n",
       "46  dataset/img6.jpg  dataset/img8.jpg  Different Persons  0.717417   \n",
       "47  dataset/img6.jpg  dataset/img9.jpg  Different Persons  0.741129   \n",
       "\n",
       "    euclidean  euclidean_l2   angular  cosine_confidence  \\\n",
       "38  15.971380      1.351062  0.472171           3.094661   \n",
       "39  16.535808      1.394017  0.490972           2.056160   \n",
       "40  13.744342      1.198475  0.409059           9.632836   \n",
       "41  14.418712      1.252662  0.430888           6.713687   \n",
       "42  14.123269      1.227314  0.420605           7.987852   \n",
       "43  14.878502      1.288234  0.445550           5.177442   \n",
       "44  13.643271      1.200943  0.410041           9.483595   \n",
       "45  13.777185      1.208136  0.412909           9.057935   \n",
       "46  13.921246      1.197846  0.408809           9.671131   \n",
       "47  14.200127      1.217481  0.416650           8.524856   \n",
       "\n",
       "    euclidean_confidence  euclidean_l2_confidence  angular_confidence  \n",
       "38              3.364286                 3.127406            5.404447  \n",
       "39              2.406347                 2.238199            4.092357  \n",
       "40              9.327166                 7.763610           10.860674  \n",
       "41              7.083039                 5.811885            8.776472  \n",
       "42              8.012303                 6.677761            9.730812  \n",
       "43              5.790447                 4.724925            7.497093  \n",
       "44              9.702528                 7.666288           10.762127  \n",
       "45              9.207479                 7.387416           10.476942  \n",
       "46              8.695498                 7.788549           10.885836  \n",
       "47              7.762737                 7.035554           10.110791  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"actual\"] == \"Different Persons\"][[\n",
    "    \"file_x\",\n",
    "    \"file_y\",\n",
    "    \"actual\",\n",
    "    \"cosine\",\n",
    "    \"euclidean\",\n",
    "    \"euclidean_l2\",\n",
    "    \"angular\",\n",
    "    \"cosine_confidence\",\n",
    "    \"euclidean_confidence\",\n",
    "    \"euclidean_l2_confidence\",\n",
    "    \"angular_confidence\",\n",
    "]].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We should see that same person classifications should be distributed between 51-100\n",
    "\n",
    "and different persons classification should be distributed between 0-49"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for distance_metric in distance_metrics:\n",
    "    df[df.actual == \"Same Person\"][f\"{distance_metric}_confidence\"].plot.kde(label=\"Same Person\")\n",
    "    df[df.actual == \"Different Persons\"][f\"{distance_metric}_confidence\"].plot.kde(label=\"Different Persons\")\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "    "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
